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<h2 id="SemEval"><a href="#SemEval" class="headerlink" title="SemEval"></a>SemEval</h2><h3 id="SemEval-2014-Task-4-Aspect-Based-Sentiment-Analysis"><a href="#SemEval-2014-Task-4-Aspect-Based-Sentiment-Analysis" class="headerlink" title="SemEval-2014 Task 4: Aspect Based Sentiment Analysis"></a>SemEval-2014 Task 4: Aspect Based Sentiment Analysis</h3><p>任务的介绍主要参考了<a target="_blank" rel="noopener" href="https://www.aclweb.org/anthology/S14-2004.pdf">SemEval-2014 Task 4: Aspect Based Sentiment Analysis</a>，官方网站为<a target="_blank" rel="noopener" href="http://alt.qcri.org/semeval2014/task4/">SemEval-2014 Task 4</a>。</p>
<p>这是SemEval-2014语义评测任务的第4个任务，它又包含4个子任务。</p>
<h4 id="子任务1：Aspect-term-extraction"><a href="#子任务1：Aspect-term-extraction" class="headerlink" title="子任务1：Aspect term extraction"></a>子任务1：Aspect term extraction</h4><p>给定针对某个entity(比如餐馆)的一些句子，识别其中的aspect term。比如句子”The food was nothing much, but I loved the staff”，我们需要识别”food”和”staff”这两个aspect term。一个句子里可能会出现多个(或者零个)aspect term。另外aspect term可能包含多个词，比如”The hard disk is very noisy”，这里的aspect term是”hard disk”。</p>
<h4 id="子任务2：Aspect-term的极性分类"><a href="#子任务2：Aspect-term的极性分类" class="headerlink" title="子任务2：Aspect term的极性分类"></a>子任务2：Aspect term的极性分类</h4><p>给定一个句子和这个句子里的所有aspect term，判定每一个term的情感极性。可能的极性包括正面(positive)、负面(negative)、中性(neutral)和冲突(conflict)。比如：</p>
<p>“I loved their <strong>fajitas</strong>” → {fajitas: positive}<br>“I hated their <strong>fajitas</strong>, but their <strong>salads</strong> were great” → {fajitas: negative, salads: positive}<br>“The <strong>fajitas</strong> are their first plate” → {fajitas: neutral}<br>“The <strong>fajitas</strong> were great to taste, but not to see” → {fajitas: conflict}</p>
<p>冲突的意思是在这个句子里既有正面的评价也有负面的评价，比如上面的第四个句子。</p>
<h4 id="子任务3：Aspect类别-category-识别"><a href="#子任务3：Aspect类别-category-识别" class="headerlink" title="子任务3：Aspect类别(category)识别"></a>子任务3：Aspect类别(category)识别</h4><p>因为很多不同的aspect term都可以归为一类，比如fajitas和salads都是餐馆的菜品，我们希望把它们都归类到food。这个任务定义了几个类别，比如餐馆(restaurant)的数据集上定义里food, service, price, ambience, anecdotes/miscellaneous等5个类别。这个任务为：给定一个句子，识别出其中的类别(注意一个句子可能包含多个类别)。比如：</p>
<p>“The restaurant was too expensive”  → {<strong>price</strong>}<br>“The restaurant was expensive, but the menu was great” → {<strong>price</strong>, <strong>food</strong>}</p>
<p>有的读者可能回想，如果能识别aspect term，然后再判断aspect term是哪个category。这可能有一个问题，对于隐式的aspect，可能只有形容词而没有名词，比如第一个句子没有price这样的aspect term，我们需要根据形容词expensive来推测类别为price。</p>
<h4 id="子任务4：Aspect类别的情感分类"><a href="#子任务4：Aspect类别的情感分类" class="headerlink" title="子任务4：Aspect类别的情感分类"></a>子任务4：Aspect类别的情感分类</h4><p>给定一个句子以及句子里的一个或者多个aspect类别，输出每个类别的情感分类。和前面的term分类一样，这里的分类也是正面(positive)、负面(negative)、中性(neutral)和冲突(conflict)。比如：</p>
<p>“The restaurant was too expensive” → {price: negative}<br>“The restaurant was expensive, but the menu was great” → {price: negative, food: positive}</p>
<p>对于上面的第一个例子，输入是句子和negative与food两个类别，输出是这两个类别的极性。</p>
<h4 id="示例数据"><a href="#示例数据" class="headerlink" title="示例数据"></a>示例数据</h4><p>全部数据可以在<a target="_blank" rel="noopener" href="http://alt.qcri.org/semeval2014/task4/index.php?id=data-and-tools">这里</a>下载，它包括餐馆和笔记本电脑两个数据集，其中餐馆数据集包含上面的4个子任务的标注，而笔记本电脑的数据只有前两个任务的标注数据(没有类别的标注)。</p>
<p>下面是餐馆的一个示例数据：</p>
<figure class="highlight plaintext"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br></pre></td><td class="code"><pre><span class="line">&lt;sentence id=&quot;813&quot;&gt;</span><br><span class="line">          &lt;text&gt;All the appetizers and salads were fabulous, the steak was mouth watering and the pasta was delicious!!!&lt;/text&gt;</span><br><span class="line">          &lt;aspectTerms&gt;</span><br><span class="line">                    &lt;aspectTerm term=&quot;appetizers&quot; polarity=&quot;positive&quot; from=&quot;8&quot; to=&quot;18&quot;/&gt;</span><br><span class="line">                    &lt;aspectTerm term=&quot;salads&quot; polarity=&quot;positive&quot; from=&quot;23&quot; to=&quot;29&quot;/&gt;</span><br><span class="line">                    &lt;aspectTerm term=&quot;steak&quot; polarity=&quot;positive&quot; from=&quot;49&quot; to=&quot;54&quot;/&gt;</span><br><span class="line">                    &lt;aspectTerm term=&quot;pasta&quot; polarity=&quot;positive&quot; from=&quot;82&quot; to=&quot;87&quot;/&gt;</span><br><span class="line">          &lt;/aspectTerms&gt;</span><br><span class="line">          &lt;aspectCategories&gt;</span><br><span class="line">                    &lt;aspectCategory category=&quot;food&quot; polarity=&quot;positive&quot;/&gt;</span><br><span class="line">          &lt;/aspectCategories&gt;</span><br><span class="line">&lt;/sentence&gt;</span><br></pre></td></tr></table></figure>
<p>下面是笔记本电脑的示例：</p>
<figure class="highlight plaintext"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br></pre></td><td class="code"><pre><span class="line">&lt;sentence id=&quot;353&quot;&gt;</span><br><span class="line">          &lt;text&gt;From the build quality to the performance, everything about it has been sub-par from what I would have expected from Apple.&lt;/text&gt;</span><br><span class="line">          &lt;aspectTerms&gt;</span><br><span class="line">                    &lt;aspectTerm term=&quot;build quality&quot; polarity=&quot;negative&quot; from=&quot;9&quot; to=&quot;22&quot;/&gt;</span><br><span class="line">                    &lt;aspectTerm term=&quot;performance&quot; polarity=&quot;negative&quot; from=&quot;30&quot; to=&quot;41&quot;/&gt;</span><br><span class="line">          &lt;/aspectTerms&gt;</span><br><span class="line">&lt;/sentence&gt;</span><br></pre></td></tr></table></figure>
<h3 id="SemEval-2015-Task-12-Aspect-Based-Sentiment-Analysis"><a href="#SemEval-2015-Task-12-Aspect-Based-Sentiment-Analysis" class="headerlink" title="SemEval-2015 Task 12: Aspect Based Sentiment Analysis"></a>SemEval-2015 Task 12: Aspect Based Sentiment Analysis</h3><p>任务的介绍主要参考了<a target="_blank" rel="noopener" href="https://www.aclweb.org/anthology/S15-2082.pdf">SemEval-2015 Task 12: Aspect Based Sentiment Analysis</a>，官方网站为<a target="_blank" rel="noopener" href="http://alt.qcri.org/semeval2015/task12/">SemEval-2015 Task 12</a>。</p>
<p>这是SemEval-2014任务的任务假定评论的都是给定实体(餐馆或者笔记本电脑)的某个属性，但是我们也可能点评这个实体的部件，比如笔记本电脑的鼠标。前面介绍过，aspect更加通用的表示方法是一棵树。不过这里的任务还是简化里一些，认为这棵树最多两层，树根是餐馆或者笔记本电脑，我们可以点评电脑的属性(比如价格)，也可以点评部件鼠标的属性(比如鼠标的灵敏度)。此外，有一些点评aspect的句子并不见得会出现对应的名词，比如下面的文字：</p>
<figure class="highlight plaintext"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">They sent it back with a huge crack in it and it still didn&#x27;t work; and that was the fourth time I’ve sent it to them to get fixed</span><br></pre></td></tr></table></figure>
<p>它点评的实体是餐馆的服务(service)，属性是服务的质量(quality)，但是文字中没有任何service或者quality相关的文字。这和前面的expensive的句子类似的。因此2015年的任务预定义里所有的Entity和属性，然后让我们识别文本中出现里哪些实体和属性的组合，也就是E#A。比如上面的句子，输出就是service#quality。另外这个任务的输入不是一个一个的句子，而是整段评论，这样我们可以利用上下文信息。当然标注和识别的粒度还是句子，只不过我们的算法可以(但大部分算法都没有)利用上下文的信息。</p>
<h4 id="任务1：In-domain任务"><a href="#任务1：In-domain任务" class="headerlink" title="任务1：In-domain任务"></a>任务1：In-domain任务</h4><p>给定一个完整的评论，我们需要完成如下3个子任务。</p>
<h5 id="Aspect类别识别"><a href="#Aspect类别识别" class="headerlink" title="Aspect类别识别"></a>Aspect类别识别</h5><p>识别评论里所有的实体(E)和属性(A)对。E和A都是预定义集合中的某一个值，比如餐馆数据集，E包含laptop, keyboard, operating system, restaurant, food, drinks等实体和performance, design, price, quality等属性。</p>
<p>更具体的，对于笔记本电脑数据集来说，E共用22个实体类别(比如LAPTOP, DISPLAY, CPU, MOTHERBOARD, HARD DISC, MEMORY, BATTERY等)和9个属性标签(比如GENERAL, PRICE, QUALITY, OPERATION_PERFORMANCE等)。完整的实体列表和属性标签列表可以参考<a target="_blank" rel="noopener" href="http://alt.qcri.org/semeval2015/task12/data/uploads/semeval2015_absa_laptops_annotationguidelines.pdf">这里</a>，下面是一些示例：</p>
<figure class="highlight plaintext"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br></pre></td><td class="code"><pre><span class="line">(1)  It fires up in the morning in less than 30 seconds and I have never had any issues with it freezing. → &#123;LAPTOP#OPERATION_PERFORMANCE&#125;</span><br><span class="line">(2)  Sometimes you will be moving your finger and the pointer will not even move.  → &#123;MOUSE#OPERATION_PERFORMANCE&#125;</span><br><span class="line">(3)  The backlit keys are wonderful when you are working in the dark.  → &#123;KEYBOARD#DESIGN_FEATURES&#125;</span><br><span class="line">(4)  I dislike the quality and the placement of the speakers. &#123;MULTIMEDIA DEVICES#QUALITY&#125;, &#123;MULTIMEDIA DEVICES#DESIGN_FEATURES&#125;</span><br><span class="line">(5)  The applications are also very easy to find and maneuver.  → &#123;SOFTWARE#USABILITY&#125;</span><br><span class="line">(6)  I took it to the shop and they said it would cost too much to repair it.  → &#123;SUPPORT#PRICE&#125;</span><br><span class="line">(7)  It is extremely portable and easily connects to WIFI at the library and elsewhere. → &#123;LAPTOP#PORTABILITY&#125;, &#123;LAPTOP#CONNECTIVITY&#125;</span><br></pre></td></tr></table></figure>
<p>比如第一个句子是说笔记本的操作响应很快，而第二个是说鼠标的操作很不灵敏。</p>
<p>对于餐馆数据集来说，E有6个实体类别(RESTAURANT, FOOD, DRINKS, SERVICE, AMBIENCE, LOCATION)和5个属性标签(GENERAL, PRICES, QUALITY, STYLE_OPTIONS, MISCELLANEOUS)，详细信息可以参考<a target="_blank" rel="noopener" href="http://alt.qcri.org/semeval2015/task12/data/uploads/semeval2015_absa_restaurants_annotationguidelines.pdf">这里</a>，下面是一些示例：</p>
<figure class="highlight plaintext"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br></pre></td><td class="code"><pre><span class="line">(1) Great for a romantic evening, but over-priced. → &#123;AMBIENCE#GENERAL&#125;, &#123;RESTAURANT#PRICES&#125;</span><br><span class="line">(2) The fajitas were delicious, but expensive. → &#123;FOOD#QUALITY&#125;, &#123;FOOD# PRICES&#125;</span><br><span class="line">(3)The exotic food is beautifully presented and is a delight in delicious combinations. → &#123;FOOD#STYLE_OPTIONS&#125;, &#123;FOOD#QUALITY&#125;</span><br><span class="line">(4) The atmosphere isn&#x27;t the greatest , but I suppose that&#x27;s how they keep the prices down. → &#123;AMBIENCE#GENERAL&#125;, &#123;RESTAURANT# PRICES&#125;</span><br><span class="line">(5) The staff is incredibly helpful and attentive. → &#123;SERVICE# GENERAL&#125;</span><br></pre></td></tr></table></figure>
<h5 id="Opinion-Target-Expression（OTE-识别"><a href="#Opinion-Target-Expression（OTE-识别" class="headerlink" title="Opinion Target Expression（OTE)识别"></a>Opinion Target Expression（OTE)识别</h5><p>这个任务只有餐馆数据集上有标注数据。OTE任务的输入是所有的E#A对，需要识别E#A对里实体E对应的字符串。当隐式的表达实体时用特殊的”NULL”表示，比如代词”它”这样的代词，有的文本甚至根本找不到和E相关的字符串。下面是一些例子：</p>
<figure class="highlight plaintext"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br></pre></td><td class="code"><pre><span class="line">(1) Great for a romantic evening, but over-priced. → &#123;AMBIENCE#GENERAL, “NULL”&#125;, &#123;RESTAURANT# PRICES, “NULL”&#125;</span><br><span class="line">(2) The fajitas were delicious, but expensive. → &#123;FOOD#QUALITY, “fajitas”&#125;, &#123;FOOD# PRICES, “fajitas”&#125;</span><br><span class="line">(3) The exotic food is beautifully presented and is a delight in delicious combinations. → &#123;FOOD#STYLE_OPTIONS, “exotic food”&#125;, &#123;FOOD# QUALITY, “exotic food”&#125;</span><br><span class="line">(4) The atmosphere isn&#x27;t the greatest , but I suppose that&#x27;s how they keep the prices down. → &#123;AMBIENCE#GENERAL, “atmosphere”&#125;, &#123;RESTAURANT# PRICES, “NULL”&#125;</span><br><span class="line">(5) The staff is incredibly helpful and attentive. → &#123;SERVICE# GENERAL, “staff”&#125;</span><br></pre></td></tr></table></figure>
<p>比如在第4个句子里，they指代的是餐馆，但是它不是OTE。</p>
<h5 id="情感分类"><a href="#情感分类" class="headerlink" title="情感分类"></a>情感分类</h5><p>给定一个句子(有上下文)和所有的E#A对，判断其情感分类，可能的分类为正面、负面和中性。这和2014年的任务有所不同。下面是一些示例：</p>
<figure class="highlight plaintext"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br></pre></td><td class="code"><pre><span class="line">(1) The applications are also very easy to find and maneuver. → &#123;SOFTWARE#USABILITY,  positive&#125;</span><br><span class="line">(2) The fajitas were great to taste, but not to see”→ &#123;FOOD#QUALITY, “fajitas”, positive&#125;,  &#123;FOOD#STYLE_OPTIONS, “fajitas”, negative &#125;</span><br><span class="line">(3) We were planning to get dessert, but the waitress basically through the bill at us before we had a chance to order.  → &#123;SERVICE# GENERAL, “waitress”, negative&#125;</span><br><span class="line">(4) It does run a little warm but that is a negligible concern.  → &#123;LAPTOP#QUALITY neutral&#125;</span><br><span class="line">(5) The fajitas are nothing out of the ordinary” → &#123;FOOD#GENERAL, “fajitas”,  neutral&#125;</span><br><span class="line">(6) I bought this laptop yesterday. → &#123;&#125;</span><br><span class="line">(7) The fajitas are their first plate  → &#123;&#125;</span><br></pre></td></tr></table></figure>
<h4 id="任务2：Out-of-domain任务"><a href="#任务2：Out-of-domain任务" class="headerlink" title="任务2：Out-of-domain任务"></a>任务2：Out-of-domain任务</h4><p>增加里一个酒店的测试数据集(没有训练数据)，然后考察模型则不同领域的泛化能力。它的输入是E#A对和句子，要求我们输出这个E#A对的情感极性。</p>
<p>下面是一个完整的评论的标注数据：</p>
<figure class="highlight plaintext"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br></pre></td><td class="code"><pre><span class="line">Review id:&quot;1004293&quot;</span><br><span class="line"> Judging from previous posts this used to be a good place, but not any longer.</span><br><span class="line"> &#123;target:&quot;NULL&quot; category:&quot;RESTAURANT#GENERAL&quot; polarity:&quot;negative&quot; from:&quot;-&quot; to=&quot;-&quot;&#125;</span><br><span class="line"> We, there were four of us, arrived at noon - the place was empty - and the staff acted </span><br><span class="line"> like we were imposing on them and they were very rude.</span><br><span class="line"> &#123;target:&quot;staff&quot; category:&quot;SERVICE#GENERAL&quot; polarity:&quot;negative&quot; from:&quot;75&quot; to:&quot;80&quot;&#125;</span><br><span class="line"> They never brought us complimentary noodles, ignored repeated requests for sugar, </span><br><span class="line"> and threw our dishes on the table.</span><br><span class="line"> &#123;target:&quot;NULL&quot; category:&quot;SERVICE#GENERAL&quot; polarity:&quot;negative&quot; from:&quot;-&quot; to:&quot;-&quot;&#125;</span><br><span class="line"> The food was lousy - too sweet or too salty and the portions tiny.</span><br><span class="line"> &#123;target:&quot;food&quot; category=&quot;FOOD#QUALITY&quot; polarity=&quot;negative&quot; from:&quot;4&quot; to:&quot;8&quot;&#125;</span><br><span class="line"> &#123;target:&quot;portions&quot; category:&quot;FOOD#STYLE_OPTIONS&quot; polarity:&quot;negative&quot; from:&quot;52&quot; to:&quot;60&quot;&#125;</span><br><span class="line"> After all that, they complained to me about the small tip.</span><br><span class="line"> &#123;target:&quot;NULL&quot; category:&quot;SERVICE#GENERAL&quot; polarity:&quot;negative&quot; from:&quot;-&quot; to:&quot;-&quot;&#125;</span><br><span class="line">			</span><br><span class="line"> Avoid this place!</span><br><span class="line"> &#123;target:&quot;place&quot; category:&quot;RESTAURANT#GENERAL&quot; polarity:&quot;negative&quot; from:&quot;11&quot; to:&quot;16&quot;&#125;</span><br></pre></td></tr></table></figure>
<p>其中from和to表示OTE字符串在句子开始和结束的下标。</p>
<h3 id="SemEval-2016-Task-5-Aspect-Based-Sentiment-Analysis"><a href="#SemEval-2016-Task-5-Aspect-Based-Sentiment-Analysis" class="headerlink" title="SemEval-2016 Task 5: Aspect Based Sentiment Analysis"></a>SemEval-2016 Task 5: Aspect Based Sentiment Analysis</h3><p>任务的介绍主要参考了<a target="_blank" rel="noopener" href="https://www.aclweb.org/anthology/S16-1002.pdf">SemEval-2016 Task 5: Aspect Based Sentiment Analysis</a>，官方网站为<a target="_blank" rel="noopener" href="http://alt.qcri.org/semeval2016/task5/">SemEval-2016 Task 5</a>。</p>
<p>2016年的任务延续里2015年的任务，为它增加了新的测试数据(15年的训练数据和测试数据都变成16年的训练数据)，此外它还首次加入了英语之外的多种语言，包括中文。它包括如下几个子任务：</p>
<h4 id="句子级别的ABSA-Aspect-Based-Sentiment-Analysis"><a href="#句子级别的ABSA-Aspect-Based-Sentiment-Analysis" class="headerlink" title="句子级别的ABSA(Aspect-Based Sentiment Analysis)"></a>句子级别的ABSA(Aspect-Based Sentiment Analysis)</h4><p>给定某个实体(笔记本电脑、餐馆或者酒店)的一篇评论的一个句子，需要确定所有观点三元组的如下内容(slot)：</p>
<h5 id="Aspect-Category-Detection"><a href="#Aspect-Category-Detection" class="headerlink" title="Aspect Category Detection"></a>Aspect Category Detection</h5><p>这个任务是确定文本里所有出现的E#A对，其中E来自预定义的实体类列表，A来自预定义的属性标签列表。</p>
<h5 id="Opinion-Target-Expression-OTE"><a href="#Opinion-Target-Expression-OTE" class="headerlink" title="Opinion Target Expression (OTE)"></a>Opinion Target Expression (OTE)</h5><p>和上年的任务一样，需要确定每个E#A对里实体对应的字符串的开始和结束下标，如果找不到则输出”NULL”。只有餐馆的数据有这个子任务。</p>
<h5 id="情感极性"><a href="#情感极性" class="headerlink" title="情感极性"></a>情感极性</h5><p>判断每一个E#A对的情感分类，类别包括正面、负面和中性。</p>
<p>上面的任务在人工标注时每次处理一个句子，但是会参考它的前后上下文的其它句子。下面是一个笔记本的评论的标注示例：</p>
<figure class="highlight plaintext"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br></pre></td><td class="code"><pre><span class="line">S1:The So called laptop Runs to Slow and I hate it! →</span><br><span class="line">&#123;LAPTOP#OPERATION_PERFORMANCE, negative&#125;, &#123;LAPTOP#GENERAL, negative&#125;</span><br><span class="line">S2:Do not buy it! → &#123;LAPTOP#GENERAL, negative&#125;</span><br><span class="line">S3:It is the worst laptop ever. → &#123;LAPTOP#GENERAL, negative&#125;</span><br></pre></td></tr></table></figure>
<p>下面是餐馆的数据：</p>
<figure class="highlight plaintext"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br></pre></td><td class="code"><pre><span class="line">S1:I was very disappointed with this restaurant. →</span><br><span class="line">&#123;RESTAURANT#GENERAL, “restaurant”, negative, from=&quot;34&quot; to=&quot;44&quot;&#125;</span><br><span class="line">S2:I’ve asked a cart attendant for a lotus leaf wrapped rice and she replied back rice and just walked away. →&#123;SERVICE#GENERAL, “cart attendant”, negative, from=&quot;12&quot; to=&quot;26&quot;&#125;</span><br><span class="line">S3:I had to ask her three times before she finally came back with the dish I’ve requested. →</span><br><span class="line">&#123;SERVICE#GENERAL, “NULL”, negative&#125;</span><br><span class="line">S4:Food was okay, nothing great. →</span><br><span class="line">&#123;FOOD#QUALITY, “Food”, neutral, from=&quot;0&quot; to=&quot;4&quot;&#125;</span><br><span class="line">S5:Chow fun was dry; pork shu mai was more than usually greasy and had to share a table with loud and rude family. →</span><br><span class="line">&#123;FOOD#QUALITY, “Chow fun”, negative, from=&quot;0&quot; to=&quot;8&quot;&#125;,</span><br><span class="line">&#123;FOOD#QUALITY, “pork shu mai”, negative, from=&quot;18&quot; to=&quot;30&quot;&#125;,</span><br><span class="line">&#123;AMBIENCE#GENERAL, “NULL”, negative&#125;</span><br><span class="line">S6:I/we will never go back to this place again. →</span><br><span class="line">&#123;RESTAURANT#GENERAL, “place”, negative, from=&quot;32&quot; to=&quot;37&quot;&#125;</span><br></pre></td></tr></table></figure>
<h4 id="文本级别的ABSA"><a href="#文本级别的ABSA" class="headerlink" title="文本级别的ABSA"></a>文本级别的ABSA</h4><p>上面的句子级别的问题是模型不能参考上下文(人工标注是参考了的)，因此还有一个文本级别的ABSA任务。它的任务和前面是一样的，只不过输入是整个评论文本，下面是一些示例。</p>
<p>下面是整个评论文本：</p>
<figure class="highlight plaintext"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br></pre></td><td class="code"><pre><span class="line">Review id:LPT1 (Laptop)</span><br><span class="line">&quot;The So called laptop Runs to Slow and I hate it! Do not buy it! It is the worst laptop ever.&quot;</span><br></pre></td></tr></table></figure>
<p>期望的输出(标注)为：<br><figure class="highlight plaintext"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br></pre></td><td class="code"><pre><span class="line">&#123;LAPTOP#OPERATION_PERFORMANCE, negative&#125;</span><br><span class="line">&#123;LAPTOP#GENERAL, negative&#125;</span><br></pre></td></tr></table></figure></p>
<p>但是它并不能简单的把文本分成句子，然后把所有句子的结果合并起来，因为一个段落里可能有多个句子都在说同一个E#A对，如果是这样的话需要判断最主要的情感倾向，比如下面的例子：</p>
<figure class="highlight plaintext"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br></pre></td><td class="code"><pre><span class="line">Review id:RST1 (Restaurant)</span><br><span class="line">&quot;I was very disappointed with this restaurant. I’ve asked a cart attendant for a lotus leaf wrapped rice and she replied back rice and just walked away. I had to ask her three times before she finally came back with the dish I’ve requested. Food was okay, nothing great. Chow fun was dry; pork shu mai was more than usually greasy and had to share a table with loud and rude family. I/we will never go back to this place again.&quot;</span><br></pre></td></tr></table></figure>
<p>它的输出为：</p>
<figure class="highlight plaintext"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br></pre></td><td class="code"><pre><span class="line">&#123;RESTAURANT#GENERAL, negative&#125;</span><br><span class="line">&#123;SERVICE#GENERAL, negative&#125;</span><br><span class="line">&#123;FOOD#QUALITY, negative&#125;</span><br><span class="line">&#123;AMBIENCE#GENERAL, negative&#125;</span><br></pre></td></tr></table></figure>
<p>它就是前面句子级别的同一段文本，关于FOOD#QUALITY有一个中性两个负面的，因此总的情感倾向是负面的。如果多个句子的情感倾向是冲突的，比如一个正面一个负面，则需要识别为冲突(conflict)。比如下面的例子：</p>
<figure class="highlight plaintext"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br></pre></td><td class="code"><pre><span class="line">Review id: RST2 (Restaurant)</span><br><span class="line">“This little place has a cute interior decor and affordable city prices. The pad seew chicken was delicious, however the pad thai was far too oily. I would just ask for no oil next time.”</span><br></pre></td></tr></table></figure>
<p>它的输出为：</p>
<figure class="highlight plaintext"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br></pre></td><td class="code"><pre><span class="line">&#123;AMBIENCE#GENERAL, positive&#125;</span><br><span class="line">&#123;RESTAURANT#PRICES, positive&#125;</span><br><span class="line">&#123;FOOD#QUALITY, conflict&#125;</span><br><span class="line">&#123;RESTAURANT#GENERAL, positive&#125;</span><br></pre></td></tr></table></figure>
<p>FOOD#QUALITY既有正面的又有负面的，因此标注为冲突。</p>
<h4 id="Out-of-domain-ABSA"><a href="#Out-of-domain-ABSA" class="headerlink" title="Out-of-domain ABSA"></a>Out-of-domain ABSA</h4><p>这个任务的测试数据的领域没有训练数据，它考察的是模型在不同领域的泛化能力。</p>
<h2 id="IMDB"><a href="#IMDB" class="headerlink" title="IMDB"></a>IMDB</h2><p>电影的评论数据，二分类任务，包括25,000个训练数据和25,000个测试数据。可以在<a target="_blank" rel="noopener" href="http://ai.stanford.edu/~amaas/data/sentiment/">这里</a>下载。</p>
</article><div class="post-copyright"><div class="post-copyright__author"><span class="post-copyright-meta">文章作者: </span><span class="post-copyright-info"><a href="https://kilogrand.gitee.io">kiloGrand</a></span></div><div class="post-copyright__type"><span class="post-copyright-meta">文章链接: </span><span class="post-copyright-info"><a href="https://kilogrand.gitee.io/2022/10/01/nlp-sentiment-analysis-dataset/">https://kilogrand.gitee.io/2022/10/01/nlp-sentiment-analysis-dataset/</a></span></div><div class="post-copyright__notice"><span class="post-copyright-meta">版权声明: </span><span class="post-copyright-info">本博客所有文章除特别声明外，均采用 <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/" target="_blank">CC BY-NC-SA 4.0</a> 许可协议。转载请注明来自 <a href="https://kilogrand.gitee.io" target="_blank">kiloGrand</a>！</span></div></div><div class="tag_share"><div class="post-meta__tag-list"><a class="post-meta__tags" href="/tags/sentiment-analysis/">sentiment analysis</a></div><div 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is-center"><div class="date"><i class="far fa-calendar-alt fa-fw"></i> 2022-10-03</div><div class="title">BERT在情感分析ATSC子任务的应用</div></div></a></div><div><a href="/2022/10/02/nlp-sentiment-analysis-survey/" title="情感分析简介"><img class="cover" src="/img/coding.jpg" alt="cover"><div class="content is-center"><div class="date"><i class="far fa-calendar-alt fa-fw"></i> 2022-10-02</div><div class="title">情感分析简介</div></div></a></div></div></div></div><div class="aside-content" id="aside-content"><div class="card-widget card-info"><div class="is-center"><div class="avatar-img"><img src="/img/profile.png" onerror="this.onerror=null;this.src='/img/friend_404.gif'" alt="avatar"/></div><div class="author-info__name">kiloGrand</div><div class="author-info__description">coder && data-science researcher</div></div><div class="card-info-data site-data is-center"><a href="/archives/"><div class="headline">文章</div><div class="length-num">46</div></a><a href="/tags/"><div class="headline">标签</div><div class="length-num">6</div></a><a href="/categories/"><div class="headline">分类</div><div class="length-num">5</div></a></div><a id="card-info-btn" target="_blank" rel="noopener" href="https://github.com/kiloGrand/"><i class="fab fa-github"></i><span>Follow Me</span></a></div><div class="card-widget card-announcement"><div class="item-headline"><i class="fas fa-bullhorn fa-shake"></i><span>公告</span></div><div class="announcement_content">This is my Blog</div></div><div class="sticky_layout"><div class="card-widget" id="card-toc"><div class="item-headline"><i class="fas fa-stream"></i><span>目录</span><span class="toc-percentage"></span></div><div class="toc-content"><ol class="toc"><li class="toc-item toc-level-2"><a class="toc-link" href="#SemEval"><span class="toc-number">1.</span> <span class="toc-text">SemEval</span></a><ol class="toc-child"><li class="toc-item toc-level-3"><a class="toc-link" href="#SemEval-2014-Task-4-Aspect-Based-Sentiment-Analysis"><span class="toc-number">1.1.</span> <span class="toc-text">SemEval-2014 Task 4: Aspect Based Sentiment Analysis</span></a><ol class="toc-child"><li class="toc-item toc-level-4"><a class="toc-link" href="#%E5%AD%90%E4%BB%BB%E5%8A%A11%EF%BC%9AAspect-term-extraction"><span class="toc-number">1.1.1.</span> <span class="toc-text">子任务1：Aspect term extraction</span></a></li><li class="toc-item toc-level-4"><a class="toc-link" href="#%E5%AD%90%E4%BB%BB%E5%8A%A12%EF%BC%9AAspect-term%E7%9A%84%E6%9E%81%E6%80%A7%E5%88%86%E7%B1%BB"><span class="toc-number">1.1.2.</span> <span class="toc-text">子任务2：Aspect term的极性分类</span></a></li><li class="toc-item toc-level-4"><a class="toc-link" href="#%E5%AD%90%E4%BB%BB%E5%8A%A13%EF%BC%9AAspect%E7%B1%BB%E5%88%AB-category-%E8%AF%86%E5%88%AB"><span class="toc-number">1.1.3.</span> <span class="toc-text">子任务3：Aspect类别(category)识别</span></a></li><li class="toc-item toc-level-4"><a class="toc-link" href="#%E5%AD%90%E4%BB%BB%E5%8A%A14%EF%BC%9AAspect%E7%B1%BB%E5%88%AB%E7%9A%84%E6%83%85%E6%84%9F%E5%88%86%E7%B1%BB"><span class="toc-number">1.1.4.</span> <span class="toc-text">子任务4：Aspect类别的情感分类</span></a></li><li class="toc-item toc-level-4"><a class="toc-link" href="#%E7%A4%BA%E4%BE%8B%E6%95%B0%E6%8D%AE"><span class="toc-number">1.1.5.</span> <span class="toc-text">示例数据</span></a></li></ol></li><li class="toc-item toc-level-3"><a class="toc-link" href="#SemEval-2015-Task-12-Aspect-Based-Sentiment-Analysis"><span class="toc-number">1.2.</span> <span class="toc-text">SemEval-2015 Task 12: Aspect Based Sentiment Analysis</span></a><ol class="toc-child"><li class="toc-item toc-level-4"><a class="toc-link" href="#%E4%BB%BB%E5%8A%A11%EF%BC%9AIn-domain%E4%BB%BB%E5%8A%A1"><span class="toc-number">1.2.1.</span> <span class="toc-text">任务1：In-domain任务</span></a><ol class="toc-child"><li class="toc-item toc-level-5"><a class="toc-link" href="#Aspect%E7%B1%BB%E5%88%AB%E8%AF%86%E5%88%AB"><span class="toc-number">1.2.1.1.</span> <span class="toc-text">Aspect类别识别</span></a></li><li class="toc-item toc-level-5"><a class="toc-link" href="#Opinion-Target-Expression%EF%BC%88OTE-%E8%AF%86%E5%88%AB"><span class="toc-number">1.2.1.2.</span> <span class="toc-text">Opinion Target Expression（OTE)识别</span></a></li><li class="toc-item toc-level-5"><a class="toc-link" href="#%E6%83%85%E6%84%9F%E5%88%86%E7%B1%BB"><span class="toc-number">1.2.1.3.</span> <span class="toc-text">情感分类</span></a></li></ol></li><li class="toc-item toc-level-4"><a class="toc-link" href="#%E4%BB%BB%E5%8A%A12%EF%BC%9AOut-of-domain%E4%BB%BB%E5%8A%A1"><span class="toc-number">1.2.2.</span> <span class="toc-text">任务2：Out-of-domain任务</span></a></li></ol></li><li class="toc-item toc-level-3"><a class="toc-link" href="#SemEval-2016-Task-5-Aspect-Based-Sentiment-Analysis"><span class="toc-number">1.3.</span> <span class="toc-text">SemEval-2016 Task 5: Aspect Based Sentiment Analysis</span></a><ol class="toc-child"><li class="toc-item toc-level-4"><a class="toc-link" href="#%E5%8F%A5%E5%AD%90%E7%BA%A7%E5%88%AB%E7%9A%84ABSA-Aspect-Based-Sentiment-Analysis"><span class="toc-number">1.3.1.</span> <span class="toc-text">句子级别的ABSA(Aspect-Based Sentiment Analysis)</span></a><ol class="toc-child"><li class="toc-item toc-level-5"><a class="toc-link" href="#Aspect-Category-Detection"><span class="toc-number">1.3.1.1.</span> <span class="toc-text">Aspect Category Detection</span></a></li><li class="toc-item toc-level-5"><a class="toc-link" href="#Opinion-Target-Expression-OTE"><span class="toc-number">1.3.1.2.</span> <span class="toc-text">Opinion Target Expression (OTE)</span></a></li><li class="toc-item toc-level-5"><a class="toc-link" href="#%E6%83%85%E6%84%9F%E6%9E%81%E6%80%A7"><span class="toc-number">1.3.1.3.</span> <span class="toc-text">情感极性</span></a></li></ol></li><li class="toc-item toc-level-4"><a class="toc-link" href="#%E6%96%87%E6%9C%AC%E7%BA%A7%E5%88%AB%E7%9A%84ABSA"><span class="toc-number">1.3.2.</span> <span class="toc-text">文本级别的ABSA</span></a></li><li class="toc-item toc-level-4"><a class="toc-link" href="#Out-of-domain-ABSA"><span class="toc-number">1.3.3.</span> <span class="toc-text">Out-of-domain ABSA</span></a></li></ol></li></ol></li><li class="toc-item toc-level-2"><a class="toc-link" href="#IMDB"><span class="toc-number">2.</span> <span class="toc-text">IMDB</span></a></li></ol></div></div><div class="card-widget card-recent-post"><div class="item-headline"><i class="fas fa-history"></i><span>最新文章</span></div><div class="aside-list"><div class="aside-list-item no-cover"><div class="content"><a class="title" href="/2023/03/24/kuiper_infer-L14/" title="自制深度学习框架--实现Yolov5的推理">自制深度学习框架--实现Yolov5的推理</a><time datetime="2023-03-24T12:00:00.000Z" title="发表于 2023-03-24 20:00:00">2023-03-24</time></div></div><div class="aside-list-item no-cover"><div class="content"><a class="title" href="/2023/03/23/kuiper_infer-L13/" 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